Functional abnormalities of the medial temporal lobe memory system in mild cognitive impairment and Alzheimer's disease: insights from functional MRI studies

Perfusion-weighted MRI patterns in neuropsychiatric systemic lupus erythematosus: a systematic review and meta-analysis

BACKGROUND

Neuropsychiatric Systemic Lupus Erythematosus (NPSLE) is a complex manifestation of Systemic Lupus Erythematosus (SLE) characterized by a wide range of neurological and psychiatric symptoms. This study aims to elucidate the patterns of Perfusion-Weighted MRI (PWI) in NPSLE patients compared to SLE patients without neuropsychiatric manifestations (non-NPSLE) and healthy controls (HCs).

MATERIAL AND METHODS

A systematic search was conducted in PubMed/Medline, Embase, Web of Science, and Scopus for studies utilizing PWI in NPSLE patients published through April 14, 2024. Cerebral blood flow (CBF) data from NPSLE, non-NPSLE patients, and HCs were extracted for meta-analysis, using standardized mean difference (SMD) as an estimate measure. For studies lacking sufficient data for inclusion, CBF, cerebral blood volume (CBV), and mean transit time (MTT) were reviewed qualitatively.

RESULTS

Our review included eight observational studies employing PWI techniques, including dynamic susceptibility contrast (DSC) and arterial spin labeling (ASL). The meta-analysis of NPSLE compared to non-NPSLE incorporated four studies, encompassing 104 NPSLE patients and 90 non-NPSLE patients. The results revealed an SMD of -1.42 (95% CI: -2.85-0.00, I2: 94%) for CBF in NPSLE compared to non-NPSLE.

CONCLUSION

PWI reveals informative patterns of cerebral perfusion, showing a significant reduction in mean CBF in NPSLE patients compared to non-NPSLE patients. Our qualitative synthesis highlights these changes, particularly in the frontal and temporal lobes. However, the existing data exhibits considerable heterogeneity and limitations.

Autoimmune hypothesis of Alzheimer's disease: unanswered question

Alzheimer's disease (AD) was described more than a century ago. However, there are still no effective approaches to its treatment, which may suggest that the search for the cure is not being conducted in the most productive direction. AD begins as selective impairments of declarative memory with no deficits in other cognitive functions. Therefore, understanding of the AD pathogenesis has to include the understanding of this selectivity. Currently, the main efforts aimed at prevention and treatment of AD are based on the dominating hypothesis for the AD pathogenesis: the amyloid hypothesis. But this hypothesis does not explain selective memory impairments since β-amyloid accumulates extracellularly and should be toxic to all types of cerebral neurons, not only to "memory engram neurons." To explain selective memory impairment, I propose the autoimmune hypothesis of AD, based on the analysis of risk factors for AD and molecular mechanisms of memory formation. Memory formation is associated with epigenetic modifications of chromatin in memory engram neurons and, therefore, might be accompanied by the expression of memory-specific proteins recognized by the adaptive immune system as "non-self" antigens. Normally, the brain is protected by the blood-brain barrier (BBB). All risk factors for AD provoke BBB disruptions, possibly leading to an autoimmune reaction against memory engram neurons. This reaction would make them selectively sensitive to tauopathy. If this hypothesis is confirmed, the strategies for AD prevention and treatment would be radically changed.

Early oxidative stress and DNA damage in Aβ-burdened hippocampal neurons in an Alzheimer's-like transgenic rat model

Oxidative stress is a key contributor to AD pathology. However, the earliest role of pre-plaque neuronal oxidative stress, remains elusive. Using laser microdissected hippocampal neurons extracted from McGill-R-Thy1-APP transgenic rats we found that intraneuronal amyloid beta (iAβ)-burdened neurons had increased expression of genes related to oxidative stress and DNA damage responses including Ercc2, Fancc, Sod2, Gsr, and Idh1. DNA damage was further evidenced by increased neuronal levels of XPD (Ercc2) and γH2AX foci, indicative of DNA double stranded breaks (DSBs), and by increased expression of Ercc6, Rad51, and Fen1, and decreased Sirt6 in hippocampal homogenates. We also found increased expression of synaptic plasticity genes (Grin2b (NR2B), CamkIIα, Bdnf, c-fos, and Homer1A) and increased protein levels of TOP2β. Our findings indicate that early accumulation of iAβ, prior to Aβ plaques, is accompanied by incipient oxidative stress and DSBs that may arise directly from oxidative stress or from maladaptive synaptic plasticity.

Edge-Community Entropy Is a Novel Neural Correlate of Aging and Moderator of Fluid Cognition

Decreased neuronal specificity of the brain in response to cognitive demands (i.e., neural dedifferentiation) has been implicated in age-related cognitive decline. Investigations into functional connectivity analogs of these processes have focused primarily on measuring segregation of nonoverlapping networks at rest. Here, we used an edge-centric network approach to derive entropy, a measure of specialization, from spatially overlapping communities during cognitive task fMRI. Using Human Connectome Project Lifespan data (713 participants, 36-100 years old, 55.7% female), we characterized a pattern of nodal despecialization differentially affecting the medial temporal lobe and limbic, visual, and subcortical systems. At the whole-brain level, global entropy moderated declines in fluid cognition across the lifespan and uniquely covaried with age when controlling for the network segregation metric modularity. Importantly, relationships between both metrics (entropy and modularity) and fluid cognition were age dependent, although entropy's relationship with cognition was specific to older adults. These results suggest entropy is a potentially important metric for examining how neurological processes in aging affect functional specialization at the nodal, network, and whole-brain level.

Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning

Alzheimer's disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21-76 years) and the Open Access Series of Imaging Studies (OASIS, age 64-95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82-92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.

Topology-based Clustering of Functional Brain Networks in an Alzheimer's Disease Cohort

Alzheimer's disease is a progressive neurodegenerative disease with many identifying biomarkers for diagnosis. However, whole-brain phenomena, particularly in functional MRI modalities, are not fully understood nor characterized. Here we employ the novel application of topological data analysis (TDA)-based methods of persistent homology to functional brain networks from ADNI-3 cohort to perform a subtyping experiment using unsupervised clustering techniques. We then investigate variations in QT-PAD challenge features across the identified clusters. Using a Wasserstein distance kernel with a variety of clustering algorithms, we found that the 0th-homology Wasserstein distance kernel and spectral clustering yielded clusters with significant differences in whole brain and medial temporal lobe (MTL) volume, thus demonstrating an intrinsic link between whole brain functional topology and brain morphometric structure. These findings demonstrate the importance of MTL in functional connectivity and the efficacy of using TDA-based machine learning methods in network neuroscience and neurodegenerative disease subtyping.

Electroencephalography-based classification of Alzheimer's disease spectrum during computer-based cognitive testing

Alzheimer's disease (AD) is a progressive disease leading to cognitive decline, and to prevent it, researchers seek to diagnose mild cognitive impairment (MCI) early. Particularly, non-amnestic MCI (naMCI) is often mistaken for normal aging as the representative symptom of AD, memory decline, is absent. Subjective cognitive decline (SCD), an intermediate step between normal aging and MCI, is crucial for prediction or early detection of MCI, which determines the presence of AD spectrum pathology. We developed a computer-based cognitive task to classify the presence or absence of AD pathology and stage within the AD spectrum, and attempted to perform multi-stage classification through electroencephalography (EEG) during resting and memory encoding state. The resting and memory-encoding states of 58 patients (20 with SCD, 10 with naMCI, 18 with aMCI, and 10 with AD) were measured and classified into four groups. We extracted features that could reflect the phase, spectral, and temporal characteristics of the resting and memory-encoding states. For the classification, we compared nine machine learning models and three deep learning models using Leave-one-subject-out strategy. Significant correlations were found between the existing neurophysiological test scores and performance of our computer-based cognitive task for all cognitive domains. In all models used, the memory-encoding states realized a higher classification performance than resting states. The best model for the 4-class classification was cKNN. The highest accuracy using resting state data was 67.24%, while it was 93.10% using memory encoding state data. This study involving participants with SCD, naMCI, aMCI, and AD focused on early Alzheimer's diagnosis. The research used EEG data during resting and memory encoding states to classify these groups, demonstrating the significance of cognitive process-related brain waves for diagnosis. The computer-based cognitive task introduced in the study offers a time-efficient alternative to traditional neuropsychological tests, showing a strong correlation with their results and serving as a valuable tool to assess cognitive impairment with reduced bias.

Visual search in Alzheimer's disease and amnestic mild cognitive impairment: An eye-tracking study

INTRODUCTION

Visual search impairment is a potential cognitive marker for Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI). The aim of this study is to compare eye movements during visual tracking in AD and aMCI patients versus healthy controls (HCs).

METHODS

A prospective cohort study included 32 AD and 37 aMCI patients, and 33 HCs. Each participant was asked to look at the target object in a visual stimulus containing one target and eight distractors, and eye movements were recorded with EyeLink 1000 Plus.

RESULTS

AD patients had fewer fixations and shorter target fixation duration than aMCI patients and HCs. Fixation durations were also shorter in aMCI patients compared to HCs. Also, AD patients were more fixated on distractors than HCs.

DISCUSSION

Our findings revealed that visual search is impaired in the early stages of AD and even aMCI, highlighting the importance of addressing visual processes in the Alzheimer's continuum.

HIGHLIGHTS

AD patients looked to distractors more and longer than the target compared to aMCI patients and older healthy individuals. aMCI patients had an impaired visual search pattern compared to healthy controls, just like patients with AD. The visual search task differentiated AD and aMCI patients from healthy individuals without dementia.

Multimodal diagnosis model of Alzheimer's disease based on improved Transformer

PURPOSE

Recent technological advancements in data acquisition tools allowed neuroscientists to acquire different modality data to diagnosis Alzheimer's disease (AD). However, how to fuse these enormous amount different modality data to improve recognizing rate and find significance brain regions is still challenging.

METHODS

The algorithm used multimodal medical images [structural magnetic resonance imaging (sMRI) and positron emission tomography (PET)] as experimental data. Deep feature representations of sMRI and PET images are extracted by 3D convolution neural network (3DCNN). An improved Transformer is then used to progressively learn global correlation information among features. Finally, the information from different modalities is fused for identification. A model-based visualization method is used to explain the decisions of the model and identify brain regions related to AD.

RESULTS

The model attained a noteworthy classification accuracy of 98.1% for Alzheimer's disease (AD) using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Upon examining the visualization results, distinct brain regions associated with AD diagnosis were observed across different image modalities. Notably, the left parahippocampal region emerged consistently as a prominent and significant brain area.

CONCLUSIONS

A large number of comparative experiments have been carried out for the model, and the experimental results verify the reliability of the model. In addition, the model adopts a visualization analysis method based on the characteristics of the model, which improves the interpretability of the model. Some disease-related brain regions were found in the visualization results, which provides reliable information for AD clinical research.

Coherence in event-related EEG oscillations in patients with Alzheimer's disease dementia and amnestic mild cognitive impairment

Objectives Working memory performances are based on brain functional connectivity, so that connectivity may be deranged in individuals with mild cognitive impairment (MCI) and patients with dementia due to Alzheimer's disease (ADD). Here we tested the hypothesis of abnormal functional connectivity as revealed by the imaginary part of coherency (ICoh) at electrode pairs from event-related electroencephalographic oscillations in ADD and MCI patients. Methods The study included 43 individuals with MCI, 43 with ADD, and 68 demographically matched healthy controls (HC). Delta, theta, alpha, beta, and gamma bands event-related ICoh was measured during an oddball paradigm. Inter-hemispheric, midline, and intra-hemispheric ICoh values were compared in ADD, MCI, and HC groups. Results The main results of the present study can be summarized as follows: (1) A significant increase of midline frontal and temporal theta coherence in the MCI group as compared to the HC group; (2) A significant decrease of theta, delta, and alpha intra-hemispheric coherence in the ADD group as compared to the HC and MCI groups; (3) A significant decrease of theta midline coherence in the ADD group as compared to the HC and MCI groups; (4) Normal inter-hemispheric coherence in the ADD and MCI groups. Conclusions Compared with the MCI and HC, the ADD group showed disrupted event-related intra-hemispheric and midline low-frequency band coherence as an estimate of brain functional dysconnectivity underlying disabilities in daily living. Brain functional connectivity during attention and short memory demands is relatively resilient in elderly subjects even with MCI (with preserved abilities in daily activities), and it shows reduced efficiency at multiple operating oscillatory frequencies only at an early stage of ADD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09920-0.

Dissociable Effects of Alzheimer's Disease-Related Cognitive Dysfunction and Aging on Functional Brain Network Segregation

Alzheimer's disease (AD) is associated with changes in large-scale functional brain network organization. Individuals with AD exhibit less segregated resting-state brain networks compared with individuals without dementia. However, declines in brain network segregation are also evident as adult individuals grow older. Determining whether these observations reflect unique or overlapping alterations on the functional connectome of the brain is essential for understanding the impact of AD on network organization and incorporating measures of functional brain network organization toward AD characterization. Relationships between AD dementia severity and participant's age on resting-state brain system segregation were examined in 326 cognitively healthy and 275 cognitively impaired human individuals recruited through the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 601; age range, 55-96 years; 320 females). Greater dementia severity and increasing age were independently associated with lower brain system segregation. Further, dementia versus age relationships with brain network organization varied according to the processing roles of brain systems and types of network interactions. Aging was associated with alterations to association systems, primarily among within-system relationships. Conversely, dementia severity was associated with alterations that included both association systems and sensory-motor systems and was most prominent among cross-system interactions. Dementia-related network alterations were evident regardless of the presence of cortical amyloid burden, revealing that the measures of functional network organization are unique from this marker of AD-related pathology. Collectively, these observations demonstrate the specific and widespread alterations in the topological organization of large-scale brain networks that accompany AD and highlight functionally dissociable brain network vulnerabilities associated with AD-related cognitive dysfunction versus aging.SIGNIFICANCE STATEMENT Alzheimer's disease (AD)-associated cognitive dysfunction is hypothesized to be a consequence of brain network damage. It is unclear exactly how brain network alterations vary with dementia severity and whether they are distinct from alterations associated with aging. We evaluated functional brain network organization measured at rest among individuals who varied in age and dementia status. AD and aging exerted dissociable impacts on the brain's functional connectome. AD-associated brain network alterations were widespread and involved systems that subserve not only higher-order cognitive operations, but also sensory and motor operations. Notably, AD-related network alterations were independent of amyloid pathology. The research furthers our understanding of AD-related brain dysfunction and motivates refining existing frameworks of dementia characterization with measures of functional network organization.

Aerobic exercise attenuates abnormal myelination and oligodendrocyte differentiation in 3xTg-AD mice

Pathological features of Alzheimer's Disease (AD) include alterations in the structure and function of neurons as well as of myelin sheaths. Accumulated evidence shows that aerobic type of exercise can enhance neuroplasticity in mouse models of AD. However, whether and how aerobic exercise can affect myelin sheath repair and neuroprotection in the AD models remains unclear. In this study we tested the hypotheses that 1) myelin structural alterations in 3xTg-AD mice would be related to abnormalities in oligodendrocyte lineage cells, resulting in impaired learning and memory, and 2) a 6-month aerobic exercise intervention would have beneficial effects on such alterations. Two-month-old male 3xTg-AD mice were randomly assigned to a control (AC) or an exercise (AE) group, and age-matched male C57BL/6;129 mice were also randomly assigned to a normal control (NC) or an exercise (NE) group, with n = 12 in each group. Mice in the exercise groups were trained on a motor-drive treadmill, 60 min per day, 5 days per week for 6 months. Cognitive function was assessed at the end of the intervention period. Then, brain specimens were obtained for assessments of morphological and oligodendrocyte lineage cell changes. The results of electron microscopy showed that myelin ultrastructure demonstrated a higher percentage of loose and granulated myelin sheath around axons in the temporal lobe in the AC, as compared with the NC group, along with greater cognitive dysfunction at 8-months of age. These differences were accompanied by significantly greater myelin basic protein (MBP) expression and less neuron-glial antigen-2 (NG2) protein and mRNA levels in the AC, compared to the NC. However, there were no significant between-group differences in the G-ratio (the ratio of axon diameter to axon plus myelin sheath diameter) and 2',3'-cyclic-nucleotide 3'-phosphodiesterase (CNPase) protein and mRNA levels. The aerobic exercise ameliorated cognitive deterioration and appeared to keep components of myelin sheath and oligodendrocyte precursor cells stabilized, resulting in a decrease in the percentage of loose and granulated myelin sheath and MBP protein, and an increase in NG2 protein and mRNA levels in the AE group. Therefore, the 6-month exercise intervention demonstrated beneficial effects on myelin lesions, abnormal differentiation of oligodendrocytes and general brain function in the 3xTg-AD mice, providing further insights into the role of aerobic exercise in management of neurodegeneration in AD by maintaining intact myelination.

Improved connectivity and cognition due to cognitive stimulation in Alzheimer's disease

Background

Due to the increasing prevalence of Alzheimer's disease (AD) and the limited efficacy of pharmacological treatment, the interest in non-pharmacological interventions, e.g., cognitive stimulation therapy (CST), to improve cognitive dysfunction and the quality of life of AD patients are on a steady rise.

Objectives

Here, we examined the efficacy of a CST program specifically conceptualized for AD dementia patients and the neural mechanisms underlying cognitive or behavioral benefits of CST.

Methods

Using neuropsychological tests and MRI-based measurements of functional connectivity, we examined the (neuro-) psychological status and network changes at two time points: pre vs. post-stimulation (8 to 12 weeks) in the intervention group (n = 15) who received the CST versus a no-intervention control group (n = 15).

Results

After CST, we observed significant improvement in the Mini-Mental State Examination (MMSE), the Alzheimer's Disease Assessment Scale, cognitive subsection (ADAS-cog), and the behavioral and psychological symptoms of dementia (BPSD) scores. These cognitive improvements were associated with an up-regulated functional connectivity between the left posterior hippocampus and the trunk of the left postcentral gyrus.

Conclusion

Our data indicate that CST seems to induce short-term global cognition and behavior improvements in mild to moderate AD dementia and enhances resting-state functional connectivity in learning- and memory-associated brain regions. These convergent results prove that even in mild to moderate dementia AD, neuroplasticity can be harnessed to alleviate cognitive impairment with CST.

Impact of transcutaneous vagus nerve stimulation on healthy cognitive and brain aging

Evidence for clinically meaningful benefits of transcutaneous vagus nerve stimulation (VNS) has been rapidly accumulating over the past 15  years. This relatively novel non-invasive brain stimulation technique has been applied to a wide range of neuropsychiatric disorders including schizophrenia, obsessive compulsive disorder, panic disorder, post-traumatic stress disorder, bipolar disorder, and Alzheimer's disease. More recently, non-invasive forms of VNS have allowed for investigations within healthy aging populations. These results offer insight into protocol considerations specific to older adults and how to translate those results into effective clinical trials and, ultimately, effective clinical care. In this review, we characterize the possible mechanisms by which non-invasive VNS may promote healthy aging (e.g., neurotransmitter effects, inflammation regulation, functional connectivity changes), special considerations for applying non-invasive VNS in an older adult population (e.g., vagus nerve changes with age), and how non-invasive VNS may be used in conjunction with existing behavioral interventions (e.g., cognitive behavioral therapy, cognitive training) to promote healthy emotional and cognitive aging.

Implications of Physical Exercise on Episodic Memory and Anxiety: The Role of the Serotonergic System

There is a growing interest in investigating the effects of physical exercise on cognitive performance, particularly episodic memory. Similarly, an increasing number of studies in recent decades have studied the effects of physical activity on mood and anxiety disorders. Moreover, the COVID-19 pandemic has raised awareness of the importance of regular physical activity for both mental and physical health. Nevertheless, the exact mechanisms underlying these effects are not fully understood. Interestingly, recent findings suggest that the serotonergic system may play a key role in mediating the effects of physical exercise on episodic memory and anxiety. In this review, we discuss the impact of physical exercise on both episodic memory and anxiety in human and animal models. In addition, we explore the accumulating evidence that supports a role for the serotonergic system in the effects of physical exercise on episodic memory and anxiety.

Neuroimaging modalities in the detection of Alzheimer's disease-associated biomarkers

Alzheimer's disease (AD) is the most common cause of dementia. Neuropathological changes in AD patients occur up to 10-20 years before the emergence of clinical symptoms. Specific diagnosis and appropriate intervention strategies are crucial during the phase of mild cognitive impairment (MCI) and AD. The detection of biomarkers has emerged as a promising tool for tracking the efficacy of potential therapies, making an early disease diagnosis, and prejudging treatment prognosis. Specifically, multiple neuroimaging modalities, including magnetic resonance imaging (MRI), positron emission tomography, optical imaging, and single photon emission-computed tomography, have provided a few potential biomarkers for clinical application. The MRI modalities described in this review include structural MRI, functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy, and arterial spin labelling. These techniques allow the detection of presymptomatic diagnostic biomarkers in the brains of cognitively normal elderly people and might also be used to monitor AD disease progression after the onset of clinical symptoms. This review highlights potential biomarkers, merits, and demerits of different neuroimaging modalities and their clinical value in MCI and AD patients. Further studies are necessary to explore more biomarkers and overcome the limitations of multiple neuroimaging modalities for inclusion in diagnostic criteria for AD.

Verbal memory formation across PET-based Braak stages of tau accumulation in Alzheimer's disease

A classical early sign of typical Alzheimer's disease is memory decline, which has been linked to the aggregation of tau in the medial temporal lobe. Verbal delayed free recall and recognition tests have consistently probed useful to detect early memory decline, and there is substantial debate on how performance, particularly in recognition tests, is differentially affected through health and disease in older adults. Using in vivo PET-Braak staging, we investigated delayed recall and recognition memory dysfunction across the Alzheimer's disease spectrum. Our cross-sectional study included 144 cognitively unimpaired elderly, 39 amyloid-β+ individuals with mild cognitive impairment and 29 amyloid-β+ Alzheimer's disease patients from the Translational Biomarkers in Aging and Dementia cohort, who underwent [¹⁸F]MK6240 tau and [¹⁸F]AZD4694 amyloid PET imaging, structural MRI and memory assessments. We applied non-parametric comparisons, correlation analyses, regression models and voxel-wise analyses. In comparison with PET-Braak Stage 0, we found that reduced, but not clinically significant, delayed recall starts at PET-Braak Stage II (adjusted P < 0.0015), and that recognition (adjusted P = 0.011) displayed a significant decline starting at PET-Braak Stage IV. While performance in both delayed recall and recognition related to tau in nearly the same cortical areas, further analyses showed that delayed recall rendered stronger associations in areas of early tau accumulation, whereas recognition displayed stronger correlations in mostly posterior neocortical regions. Our results support the notion that delayed recall and recognition deficits are predominantly associated with tau load in allocortical and neocortical areas, respectively. Overall, delayed recall seems to be more dependent on the integrity of anterior medial temporal lobe structures, while recognition appears to be more affected by tau accumulation in cortices beyond medial temporal regions.

Neuroimaging in Alzheimer's Disease for Early Diagnosis: A Comprehensive Review

Alzheimer's disease (AD) is the most common cause of dementia in the elderly, affecting roughly half of those over the age of 85. We briefly discussed the risk factors, epidemiology, and treatment options for AD. The development of therapeutic therapies operating very early in the disease cascade has been spurred by the realization that the disease process begins at least a decade or more before the manifestation of symptoms. Thus, the clinical significance of early diagnosis was emphasized. Using various keywords, a literature search was carried out using PubMed and other databases. For inclusion, pertinent articles were chosen and reviewed. This article has reviewed different neuroimaging techniques that are considered advanced tools to aid in establishing a diagnosis and highlighted the advantages as well as disadvantages of those techniques. Besides, the prevalence of several in vivo biomarkers aided in discriminating affected individuals from healthy controls in the early stages of the disease. Each imaging method has its advantages and disadvantages, hence no single imaging approach can be the optimum modality for diagnosis. This article also commented on a better approach to using these techniques to increase the likelihood of an early diagnosis.

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

Aggressive behavior and prognosis in patients with mild cognitive impairment

The diagnosis of mild cognitive impairment (MCI) is associated with an increased risk of developing dementia. When evaluating the further prognosis of MCI, the occurrence of neuropsychiatric symptoms, particularly aggressive and impulsive behavior, may play an important role.

Objective

The aim of this study was to evaluate the relationship between aggressive behavior and cognitive dysfunction in patients diagnosed with MCI.

Methods

The results are based on a 7-year prospective study. At the time of inclusion in the study, participants, recruited from an outpatient clinic, were assessed with Mini-Mental State Examination (MMSE) and the Cohen-Mansfield Agitation Inventory (CMAI). A reassessment was performed after 1 year using the MMSE scale in all patients. The time of next MMSE administration was depended on the clinical condition of patients took place at the end of follow-up, that is, at the time of diagnosis of the dementia or after 7 years from inclusion when the criteria for dementia were not met.

Results

Of the 193 patients enrolled in the study, 75 were included in the final analysis. Patients who converted to dementia during the observation period exhibited a greater severity of symptoms in each of the assessed CMAI categories. Moreover, there was a significant correlation between the global result of CMAI and the results of the physical nonaggressive and verbal aggressive subscales with cognitive decline during the first year of observation.

Conclusions

Despite several limitations to the study, aggressive and impulsive behaviors seem to be an unfavorable prognostic factor in the course of MCI.

Emotional Dysregulation in Mild Behavioral Impairment Is Associated with Reduced Cortical Thickness in the Right Supramarginal Gyrus

BACKGROUND

Mild behavioral impairment (MBI) has attracted attention as a possible precursor symptom of dementia, but its neural basis has not been fully investigated.

OBJECTIVE

We aimed to investigate the relationship between MBI and surface area, cortical thickness, and volume in the temporal and parietal lobes, which are strongly associated with dementia and emotional disorders.

METHODS

This retrospective study evaluated 123 participants: 90 with mild cognitive impairment (MCI), 13 with subjective cognitive decline (SCD), and 20 cognitively healthy (CH). Using analysis of covariance (ANCOVA) with sex, age, and MMSE score as covariates, cortical thickness, surface area, and volume in 10 regions were compared between groups with and without MBI. Groups with MBI emotional dysregulation were also compared with groups without MBI.

RESULTS

ANCOVA revealed significantly smaller cortical thickness in the MBI group's right parahippocampal (p = 0.01) and supramarginal gyri (p = 0.002). After multiple comparison correction, only the right supramarginal gyrus was significantly smaller (p = 0.02). When considering only MBI emotional dysregulation, the right parahippocampal and supramarginal gyrus' cortical thicknesses were significantly smaller in this MBI group (p = 0.03, 0.01). However, multiple comparison correction identified no significant differences (p = 0.14, 0.11).

CONCLUSION

Overall MBI and the emotional dysregulation domains were associated with reduced cortical thickness in the right parahippocampal and supramarginal gyri. Since neurodegeneration in the medial temporal and parietal lobe precedes early Alzheimer's disease (AD), MBI, particularly emotion dysregulation, may predict early AD below the diagnostic threshold.

Gray Matter Volume as Evidence for Cognitive Reserve in Bilinguals With Mild Cognitive Impairment

BACKGROUND

Compared with monolinguals, bilinguals have a later onset of mild cognitive impairment (MCI) and Alzheimer disease symptoms and greater neuropathology at similar cognitive and clinical levels. The present study follows a previous report showing the faster conversion from MCI to Alzheimer disease for bilingual patients than comparable monolinguals, as predicted by a cognitive reserve (CR).

PURPOSE

Identify whether the increased CR found for bilinguals in the previous study was accompanied by greater gray matter (GM) atrophy than was present for the monolinguals.

METHODS

A novel deep-learning technique based on convolutional neural networks was used to enhance clinical scans into 1 mm MPRAGEs and analyze the GM volume at the time of MCI diagnosis in the earlier study.

PATIENTS

Twenty-four bilingual and 24 monolingual patients were diagnosed with MCI at a hospital memory clinic.

RESULTS

Bilingual patients had more GM loss than monolingual patients in areas related to language processing, attention, decision-making, motor function, and episodic memory retrieval. Bilingualism and age were the strongest predictors of atrophy after other variables such as immigration and education were included in a multivariate model.

DISCUSSION

CR from bilingualism is evident in the initial stages of neurodegeneration after MCI has been diagnosed.

The Ability to Use Contextual Information in Object and Scene Recognition in Patients with Mild Cognitive Impairment

BACKGROUND

The ability to understand and make use of object-scene relationships are critical for object and scene recognition.

OBJECTIVE

The current study assessed whether patients with mild cognitive impairment (MCI), possibly in the preclinical phase of Alzheimer's disease, exhibited impairment in processing contextual information in scene and object recognition.

METHODS

In Experiment 1, subjects viewed images of foreground objects in either semantic consistent or inconsistent scenes under no time pressure, and they verbally reported the names of foreground objects and backgrounds. Experiment 2 replicated Experiment 1, except that subjects were required to name scene first. Experiment 3 examined object and scene recognition accuracy baselines, recognition difficulty, familiarity with objects/scenes, and object-scene consistency judgements.

RESULTS

There were contextual consistency effects on scene recognition for MCI and healthy subjects, regardless of response sequence. Scenes were recognized more accurately under the consistent condition than the inconsistent condition. Additionally, MCI patients were more susceptible to incongruent contextual information, possibly due to inhibitory deficits or over-dependence on semantic knowledge. However, no significant differences between MCI and healthy subjects were observed in consistency judgement, recognition accuracy, recognition difficulty and familiarity rating, suggesting no significant impairment in object and scene knowledge among MCI subjects.

CONCLUSIONS

The study indicates that MCI patients retain relatively intact contextual processing ability but may exhibit inhibitory deficits or over-reliance on semantic knowledge.

Etiology and Clinical Significance of Network Hyperexcitability in Alzheimer's Disease: Unanswered Questions and Next Steps

Cortical network hyperexcitability related to synaptic dysfunction in Alzheimer's disease (AD) is a potential target for therapeutic intervention. In recent years, there has been increased interest in the prevalence of silent seizures and interictal epileptiform discharges (IEDs, or seizure tendency), with both entities collectively termed "subclinical epileptiform activity" (SEA), on neurophysiologic studies in AD patients. SEA has been demonstrated to be common in AD, with prevalence estimates ranging between 22-54%. Converging lines of basic and clinical evidence imply that modifying a hyperexcitable state results in an improvement in cognition. In particular, though these results require further confirmation, post-hoc findings from a recent phase II clinical trial suggest a therapeutic effect with levetiracetam administration in patients with AD and IEDs. Here, we review key unanswered questions as well as potential clinical trial avenues. Specifically, we discuss postulated mechanisms and treatment of hyperexcitability in patients with AD, which are of interest in designing future disease-modifying therapies. Criteria to prompt screening and optimal screening methodology for hyperexcitability have yet to be defined, as does timing and personalization of therapeutic intervention.

Disruption of hippocampal neuronal circuit function depends upon behavioral state in the APP/PS1 mouse model of Alzheimer's disease

The Alzheimer's disease-associated peptide amyloid-beta (Aβ) has been associated with neuronal hyperactivity under anesthesia, but clinical trials of anticonvulsants or neural system suppressors have, so far, failed to improve symptoms in AD. Using simultaneous hippocampal calcium imaging and electrophysiology in freely moving mice expressing human Aβ, here we show that Aβ aggregates perturbed neural systems in a state-dependent fashion, driving neuronal hyperactivity in exploratory behavior and slow wave sleep (SWS), yet suppressing activity in quiet wakefulness (QW) and REM sleep. In exploratory behavior and REM sleep, Aβ impaired hippocampal theta-gamma phase-amplitude coupling and altered neuronal synchronization with theta. In SWS, Aβ reduced cortical slow oscillation (SO) power, the coordination of hippocampal sharp wave-ripples with both the SO and thalamocortical spindles, and the coordination of calcium transients with the sharp wave-ripple. Physostigmine improved Aβ-associated hyperactivity in exploratory behavior and hypoactivity in QW and expanded the range of gamma that coupled with theta phase, but exacerbated hypoactivity in exploratory behavior. Together, these findings show that the effects of Aβ alone on hippocampal circuit function are profoundly state dependent and suggest a reformulation of therapeutic strategies aimed at Aβ induced hyperexcitability.

Excessive/Aberrant and Maladaptive Synaptic Plasticity: A Hypothesis for the Pathogenesis of Alzheimer’s Disease

The amyloid hypothesis for the pathogenesis of Alzheimer’s disease (AD) is widely accepted. Last year, the US Food and Drug Administration considered amyloid-β peptide (Aβ) as a surrogate biomarker and approved an anti-Aβ antibody, aducanumab, although its effectiveness in slowing the progression of AD is still uncertain. This approval has caused a great deal of controversy. Opinions are divided about whether there is enough evidence to definitely consider Aβ as a causative substance of AD. To develop this discussion constructively and to discover the most suitable therapeutic interventions in the end, an alternative persuasive hypothesis needs to emerge to better explain the facts. In this paper, I propose a hypothesis that excessive/aberrant and maladaptive synaptic plasticity is the pathophysiological basis for AD.

A four-month home-based tDCS study on patients with Alzheimer's disease

In the present open-label study, our first aim was to study the tolerability and feasibility of long-term treatment with transcranial direct current stimulation (tDCS) and the second aim was to measure whether the treatment led to cognitive improvement. Participants with AD used a tDCS home-treatment kit inducing a low current (2 mA) via two scalp electrodes 30 minutes daily for 4 months. A total of 8 participants were recruited. The treatment technique was manageable for the participants and their spouses, and no troublesome side effects were reported. No significant effects of treatment were found after 4 months.

Young Adults with a Parent with Dementia Show Early Abnormalities in Brain Activity and Brain Volume in the Hippocampus: A Matched Case-Control Study

Having a parent with Alzheimer’s disease (AD) and related dementias confers a risk for developing these types of neurocognitive disorders in old age, but the mechanisms underlying this risk are understudied. Although the hippocampus is often one of the earliest brain regions to undergo change in the AD process, we do not know how early in the lifespan such changes might occur or whether they differ early in the lifespan as a function of family history of AD. Using a rare sample, young adults with a parent with late-onset dementia, we investigated whether brain abnormalities could already be detected compared with a matched sample. Moreover, we employed simple yet novel techniques to characterize resting brain activity (mean and standard deviation) and brain volume in the hippocampus. Young adults with a parent with dementia showed greater resting mean activity and smaller volumes in the left hippocampus compared to young adults without a parent with dementia. Having a parent with AD or a related dementia was associated with early aberrations in brain function and structure. This early hippocampal dysfunction may be due to aberrant neural firing, which may increase the risk for a diagnosis of dementia in old age.

Computerized paired associate learning performance and imaging biomarkers in older adults without dementia

This cross-sectional study examined whether performance on the computerized Paired Associate Learning (PAL) task from the Cambridge Neuropsychological Test Automated Battery is associated with amyloid positivity as measured by Positron Emission Tomography, regional volume composites as measured by Magnetic Resonance Imaging, and cognitive impairment. Participants from the BIOCARD Study (N = 73, including 62 cognitively normal and 11 with mild cognitive impairment; M age = 70 years) completed the PAL task, a comprehensive clinical and neuropsychological assessment, and neuroimaging as part of their annual study visit. In linear regressions covarying age, sex, years of education and diagnosis, higher PAL error scores were associated with amyloid positivity but not with medial temporal or cortical volume composites. By comparison, standard neuropsychological measures of episodic memory and global cognition were unrelated to amyloid positivity, but better performance on the verbal episodic memory measures was associated with larger cortical volume composites. Participants with mild cognitive impairment demonstrated worse cognitive performance on all of the cognitive measures, including the PAL task. These findings suggest that this computerized visual paired associate learning task may be more sensitive to amyloid positivity than standard neuropsychological tests, and may therefore be a promising tool for detecting amyloid positivity in non-demented participants.

Development of a novel task-based functional magnetic resonance imaging phantom based on a bubble-compression approach

PURPOSE

Phantoms used in previous functional magnetic resonance imaging (fMRI) studies have drawbacks, such as a complicated circuit and equipment use, a single signal-change rate, and T₂ * values that do not correspond to those of living human brains. We aimed to develop a phantom for use in task-based fMRI studies (gradient-echo echo-planar imaging; GRE-EPI) with bioequivalent T₁ and T₂ * values, using an innovative method to control the rate of signal change.

METHODS

A gel phantom with T₁ and T₂ * values equivalent to that of the living brain gray matter was fixed in a 150 mm diameter container, with five holes, each of which could hold a 30-mL syringe. The gel phantom contained microscopic air bubbles; this made it possible to control the percent signal change by injector-induced water pressure changes. Using this phantom, we investigated the percent signal change, derived an equation that can approximately reproduce an arbitrary percent signal change, compared different gel phantom samples, investigated the change in relaxation time and bubble size during signal change, and assessed the change in values in each sample over time.

RESULTS

The relaxation time of the gel phantom was similar to the literature values for gray matter. The percent signal change achieved was approximately 0-13.51% and was dependent on the water pressure change. The derived equation was y = 0.000008x3 - 0.000771x2 + 0.034222x - 0.026054, with y being the percent signal change and x being the pressure in kPa; the reproducibility was high. No significant difference was detected among samples of gray matter gel phantoms (P > 0.05). The change in the rate of signal change with the change in water pressure was due to the change in T₂ * value with the change in bubble size. With pressure increasing from 0 to 151.7 kPa, the T₂ * value increased from 52 ms to 85 ms. The newly developed gel phantom was stable for 60 days, but its bubble size changed after 21 days.

CONCLUSION

We developed a novel phantom for use in fMRI, which could reproduce minute signal changes similar to the blood-oxygen-level-dependent effect and with bioequivalent T₁ and T₂ * values, and used an innovative method to control the percent signal change by compressing the air contained in the phantom for validation of fMRI using GRE-EPI. This phantom reproduced the percent signal change due to changes in T₂ * values, which is very similar to scanning a human body. This phantom is expected to be a powerful tool for advancing the study of task-based fMRI. This article is protected by copyright. All rights reserved.

A Digital Screening System for Alzheimer Disease Based on a Neuropsychological Test and a Convolutional Neural Network: System Development and Validation

Background

Alzheimer disease (AD) and other types of dementia are now considered one of the world’s most pressing health problems for aging people worldwide. It was the seventh-leading cause of death, globally, in 2019. With a growing number of patients with dementia and increasing costs for treatment and care, early detection of the disease at the stage of mild cognitive impairment (MCI) will prevent the rapid progression of dementia. In addition to reducing the physical and psychological stress of patients’ caregivers in the long term, it will also improve the everyday quality of life of patients.

Objective

The aim of this study was to design a digital screening system to discriminate between patients with MCI and AD and healthy controls (HCs), based on the Rey-Osterrieth Complex Figure (ROCF) neuropsychological test.

Methods

The study took place at National Taiwan University between 2018 and 2019. In order to develop the system, pretraining was performed using, and features were extracted from, an open sketch data set using a data-driven deep learning approach through a convolutional neural network. Later, the learned features were transferred to our collected data set to further train the classifier. The first data set was collected using pen and paper for the traditional method. The second data set used a tablet and smart pen for data collection. The system’s performance was then evaluated using the data sets.

Results

The performance of the designed system when using the data set that was collected using the traditional pen and paper method resulted in a mean area under the receiver operating characteristic curve (AUROC) of 0.913 (SD 0.004) when distinguishing between patients with MCI and HCs. On the other hand, when discriminating between patients with AD and HCs, the mean AUROC was 0.950 (SD 0.003) when using the data set that was collected using the digitalized method.

Conclusions

The automatic ROCF test scoring system that we designed showed satisfying results for differentiating between patients with AD and MCI and HCs. Comparatively, our proposed network architecture provided better performance than our previous work, which did not include data augmentation and dropout techniques. In addition, it also performed better than other existing network architectures, such as AlexNet and Sketch-a-Net, with transfer learning techniques. The proposed system can be incorporated with other tests to assist clinicians in the early diagnosis of AD and to reduce the physical and mental burden on patients’ family and friends.

Decoupling of regional neural activity and inter-regional functional connectivity in Alzheimer's disease: a simultaneous PET/MR study

PURPOSE

Alzheimer's disease (AD) and mild cognitive impairment (MCI) are characterized by both aberrant regional neural activity and disrupted inter-regional functional connectivity (FC). However, the effect of AD/MCI on the coupling between regional neural activity (measured by regional fluorodeoxyglucose imaging (rFDG)) and inter-regional FC (measured by resting-state functional magnetic resonance imaging (rs-fMRI)) is poorly understood.

METHODS

We scanned 19 patients with MCI, 33 patients with AD, and 26 healthy individuals by simultaneous FDG-PET/rs-fMRI and assessed rFDG and inter-regional FC metrics (i.e., clustering coefficient and degree centrality). Next, we examined the potential moderating effect of disease status (MCI or AD) on the link between rFDG and inter-regional FC metrics using hierarchical moderated multiple regression analysis. We also tested this effect by considering interaction between disease status and inter-regional FC metrics, as well as interaction between disease status and rFDG.

RESULTS

Our findings revealed that both rFDG and inter-regional FC metrics were disrupted in MCI and AD. Moreover, AD altered the relationship between rFDG and inter-regional FC metrics. In particular, we found that AD moderated the effect of inter-regional FC metrics of the caudate, parahippocampal gyrus, angular gyrus, supramarginal gyrus, frontal pole, inferior temporal gyrus, middle frontal, lateral occipital, supramarginal gyrus, precuneus, and thalamus on predicting their rFDG. On the other hand, AD moderated the effect of rFDG of the parietal operculum on predicting its inter-regional FC metric.

CONCLUSION

Our findings demonstrated that AD decoupled the link between regional neural activity and functional segregation and global connectivity across particular brain regions.

Transcranial Direct Current Stimulation (tDCS) as a Useful Rehabilitation Strategy to Improve Cognition in Patients With Alzheimer's Disease and Parkinson's Disease: An Updated Systematic Review of Randomized Controlled Trials

Alzheimer's disease (AD) and Parkinson's disease (PD) are neurodegenerative disorders characterized by cognitive impairment and functional decline increasing with disease progression. Within non-pharmacological interventions, transcranial direct current stimulation (tDCS) might represent a cost-effective rehabilitation strategy to implement cognitive abilities with positive implications for functional autonomy and quality-of-life of patients. Our systematic review aimed at evaluating the effects of tDCS upon cognition in people suffering from AD and PD. We searched for randomized controlled trials (RCTs) into PubMed, Web of Science, and Cochrane Library. Three review authors extracted data of interest, with neuropsychological tests or experimental cognitive tasks scores as outcome measures. A total of 17 RCTs (10 trials for AD and 7 trials for PD) were included. Compared with sham stimulation, tDCS may improve global cognition and recognition memory in patients with AD and also some executive functions (i.e., divided attention, verbal fluency, and reduction of sensitivity to interference) in patients with PD. Criticism remains about benefits for the other investigated cognitive domains. Despite preliminary emerging evidences, larger RCTs with common neuropsychological measures and long-term follow-ups establishing longevity of the observed effects are necessary for future research in applied psychology field, alongside improved clinical guidelines on the neurodegenerative disorders pertaining electrodes montage, sessions number, duration and intensity of the stimulation, and cognitive battery to be used.

Past, present and future of therapeutic strategies against amyloid-β peptides in Alzheimer's disease: a systematic review

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease in ageing, affecting around 46 million people worldwide but few treatments are currently available. The etiology of AD is still puzzling, and new drugs development and clinical trials have high failure rates. Urgent outline of an integral (multi-target) and effective treatment of AD is needed. Accumulation of amyloid-β (Aβ) peptides is considered one of the fundamental neuropathological pillars of the disease, and its dyshomeostasis has shown a crucial role in AD onset. Therefore, many amyloid-targeted therapies have been investigated. Here, we will systematically review recent (from 2014) investigational, follow-up and review studies focused on anti-amyloid strategies to summarize and analyze their current clinical potential. Combination of anti-Aβ therapies with new developing early detection biomarkers and other therapeutic agents acting on early functional AD changes will be highlighted in this review. Near-term approval seems likely for several drugs acting against Aβ, with recent FDA approval of a monoclonal anti-Aβ oligomers antibody -aducanumab- raising hopes and controversies. We conclude that, development of oligomer-epitope specific Aβ treatment and implementation of multiple improved biomarkers and risk prediction methods allowing early detection, together with therapies acting on other factors such as hyperexcitability in early AD, could be the key to slowing this global pandemic.

The potential roles of excitatory-inhibitory imbalances and the repressor element-1 silencing transcription factor in aging and aging-associated diseases

Disruptions to the central excitatory-inhibitory (E/I) balance are thought to be related to aging and underlie a host of neural pathologies, including Alzheimer's disease. Aging may induce an increase in excitatory signaling, causing an E/I imbalance, which has been linked to shorter lifespans in mice, flies, and worms. In humans, extended longevity correlates to greater repression of genes involved in excitatory neurotransmission. The repressor element-1 silencing transcription factor (REST) is a master regulator in neural cells and is believed to be upregulated with senescent stimuli, whereupon it counters hyperexcitability, insulin/insulin-like signaling pathway activity, oxidative stress, and neurodegeneration. This review examines the putative mechanisms that distort the E/I balance with aging and neurodegeneration, and the putative roles of REST in maintaining neuronal homeostasis.

Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models

Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.

Preservation Analysis on Spatiotemporal Specific Co-expression Networks Suggests the Immunopathogenesis of Alzheimer's Disease

The occurrence and development of Alzheimer’s disease (AD) is a continuous clinical and pathophysiological process, molecular biological, and brain functional change often appear before clinical symptoms, but the detailed underlying mechanism is still unclear. The expression profiling of postmortem brain tissue from AD patients and controls provides evidence about AD etiopathogenesis. In the current study, we used published AD expression profiling data to construct spatiotemporal specific coexpression networks in AD and analyzed the network preservation features of each brain region in different disease stages to identify the most dramatically changed coexpression modules and obtained AD-related biological pathways, brain regions and circuits, cell types and key genes based on these modules. As result, we constructed 57 spatiotemporal specific networks (19 brain regions by three disease stages) in AD and observed universal expression changes in all 19 brain regions. The eight most dramatically changed coexpression modules were identified in seven brain regions. Genes in these modules are mostly involved in immune response-related pathways and non-neuron cells, and this supports the immune pathology of AD and suggests the role of blood brain barrier (BBB) injuries. Differentially expressed genes (DEGs) meta-analysis and protein–protein interaction (PPI) network analysis suggested potential key genes involved in AD development that might be therapeutic targets. In conclusion, our systematical network analysis on published AD expression profiling data suggests the immunopathogenesis of AD and identifies key brain regions and genes.

Multidomain Intervention for Reversal of Cognitive Frailty, Towards a Personalized Approach (AGELESS Trial): Study Design

Background:

Cognitive frailty (CF) is identified as one of the main precursors of dementia. Multidomain intervention has been found to delay or prevent the onset of CF.

Objective:

The aim of our present study is to determine the effectiveness of a comprehensive, multidomain intervention on CF; to evaluate its cost effectiveness and the factors influencing adherence toward this intensive intervention.

Methods:

A total of 1,000 community dwelling older adults, aged 60 years and above will be screened for CF. This randomized controlled trial involves recruitment of 330 older adults with CF from urban, semi-urban, and rural areas in Malaysia. Multidomain intervention comprised of physical, nutritional, cognitive, and psychosocial aspects will be provided to participants in the experimental group (n = 165). The control group (n = 165) will continue their usual care with their physician. Primary outcomes include CF status, physical function, psychosocial and nutritional status as well as cognitive performance. Vascular health and gut microbiome will be assessed using blood and stool samples. A 24-month intensive intervention will be prescribed to the participants and its sustainability will be assessed for the following 12 months. The effective intervention strategies will be integrated as a personalized telerehabilitation package for the reversal of CF for future use.

Results:

The multidomain intervention developed from this trial is expected to be cost effective compared to usual care as well as able is to reverse CF.

Conclusion:

This project will be part of the World-Wide FINGERS (Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability) Network, of which common identifiable data will be shared and harmonized among the consortia.

Identification of Electroencephalogram Signals in Alzheimer's Disease by Multifractal and Multiscale Entropy Analysis

Alzheimer's disease (AD) is the most common form of dementia and is a progressive neurodegenerative disease that primarily develops in old age. In recent years, it has been reported that early diagnosis of AD and early intervention significantly delays disease progression. Hence, early diagnosis and intervention are emphasized. As a diagnostic index for AD patients, evaluating the complexity of the dependence of the electroencephalography (EEG) signal on the temporal scale of Alzheimer's disease (AD) patients is effective. Multiscale entropy analysis and multifractal analysis have been performed individually, and their usefulness as diagnostic indicators has been confirmed, but the complemental relationship between these analyses, which may enhance diagnostic accuracy, has not been investigated. We hypothesize that combining multiscale entropy and fractal analyses may add another dimension to understanding the alteration of EEG dynamics in AD. In this study, we performed both multiscale entropy and multifractal analyses on EEGs from AD patients and healthy subjects. We found that the classification accuracy was improved using both techniques. These findings suggest that the use of multiscale entropy analysis and multifractal analysis may lead to the development of AD diagnostic tools.

Age-related dedifferentiation and hyperdifferentiation of perceptual and mnemonic representations

Preliminary evidence indicates that occipito-temporal activation patterns for different visual stimuli are less distinct in older (OAs) than younger (YAs) adults, suggesting a dedifferentiation of visual representations with aging. Yet, it is unclear if this deficit (1) affects only sensory or also categorical aspects of representations during visual perception (perceptual representations), and (2) affects only perceptual or also mnemonic representations. To investigate these issues, we fMRI-scanned YAs and OAs viewing and then remembering visual scenes. First, using representational similarity analyses, we distinguished sensory vs. categorical features of perceptual representations. We found that, compared to YAs, sensory features in early visual cortex were less differentiated in OAs (i.e., age-related dedifferentiation), replicating previous research, whereas categorical features in anterior temporal lobe (ATL) were more differentiated in OAs. This is, to our knowledge, the first report of an age-related hyperdifferentiation. Second, we assessed the quality of mnemonic representations by measuring encoding-retrieval similarity (ERS) in activation patterns. We found that aging impaired mnemonic representations in early visual cortex and hippocampus but enhanced mnemonic representations in ATL. Thus, both perceptual and mnemonic representations in ATL were enhanced by aging. In sum, our findings suggest that aging impairs visual and mnemonic representations in posterior brain regions but enhances them in anterior regions.

Brain circuitry underlying the ABC model of anxiety

Anxiety Disorders are prevalent and often chronic, recurrent conditions that reduce quality of life. The first-line treatments, such as serotonin reuptake inhibitors and cognitive behavioral therapy, leave a significant proportion of patients symptomatic. As psychiatry moves toward targeted circuit-based treatments, there is a need for a theory that unites the phenomenology of anxiety with its underlying neural circuits. The Alarm, Belief, Coping (ABC) theory of anxiety describes how the neural circuits associated with anxiety interact with each other and domains of the anxiety symptoms, both temporally and spatially. The latest advancements in neuroimaging techniques offer the ability to assess these circuits in vivo. Using Neurosynth, a large open-access meta-analytic imaging database, the association between terms related to specific neural circuits was explored within the ABC theory framework. Alarm-related terms were associated with the amygdala, anterior cingulum, insula, and bed nucleus of stria terminalis. Belief-related terms were associated with medial prefrontal cortex, precuneus, bilateral temporal poles, and hippocampus. Coping-related terms were associated with the ventrolateral and dorsolateral prefrontal cortices, basal ganglia, and anterior cingulate. Neural connections underlying the functional neuroanatomy of the ABC model were observed. Additionally, there was considerable interaction and overlap between circuits associated with the symptom domains. Further neuroimaging research is needed to explore the dynamic interaction between the functional domains of the ABC theory. This will pave the way for probing the neuroanatomical underpinnings of anxiety disorders and provide an evidence-based foundation for the development of targeted treatments, such as neuromodulation.

Splenial white matter integrity is associated with memory impairments in posterior cortical atrophy

Posterior cortical atrophy is an atypical form of Alzheimer’s disease characterized by visuospatial impairments and predominant tissue loss in the posterior parieto-occipital and temporo-occipital cortex. Whilst episodic memory is traditionally thought to be relatively preserved in posterior cortical atrophy, recent work indicates that memory impairments form a common clinical symptom in the early stages of the disease. Neuroimaging studies suggest that memory dysfunction in posterior cortical atrophy may originate from atrophy and functional hypoconnectivity of parietal cortex. The structural connectivity patterns underpinning these memory impairments, however, have not been investigated. This line of inquiry is of particular interest, as changes in white matter tracts of posterior cortical atrophy patients have been shown to be more extensive than expected based on posterior atrophy of grey matter. In this cross-sectional diffusion tensor imaging MRI study, we examine the relationship between white matter microstructure and verbal episodic memory in posterior cortical atrophy. We assessed episodic memory performance in a group of posterior cortical atrophy patients (n = 14) and a group of matched healthy control participants (n = 19) using the Free and Cued Selective Reminding Test with Immediate Recall. Diffusion tensor imaging measures were obtained for 13 of the posterior cortical atrophy patients and a second control group of 18 healthy adults. Patients and healthy controls demonstrated similar memory encoding performance, indicating that learning of verbal information was preserved in posterior cortical atrophy. However, retrieval of verbal items was significantly impaired in the patient group compared with control participants. As expected, tract-based spatial statistics analyses showed widespread reductions of white matter integrity in posterior cortical regions of patients compared with healthy adults. Correlation analyses indicated that poor verbal retrieval in the patient group was specifically associated with microstructural damage of the splenium of the corpus callosum. Post-hoc tractography analyses in healthy controls demonstrated that this splenial region was connected to thalamic radiations and the retrolenticular part of the internal capsule. These results provide insight into the brain circuits that underlie memory impairments in posterior cortical atrophy. From a cognitive perspective, we propose that the association between splenial integrity and memory dysfunction could arise indirectly via disruption of attentional processes. We discuss implications for the clinical phenotype and development of therapeutic aids for cognitive impairment in posterior cortical atrophy.

A unified framework for personalized regions selection and functional relation modeling for early MCI identification

Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely adopted to investigate functional abnormalities in brain diseases. Rs-fMRI data is unsupervised in nature because the psychological and neurological labels are coarse-grained, and no accurate region-wise label is provided along with the complex co-activities of multiple regions. To the best of our knowledge, most studies regarding univariate group analysis or multivariate pattern recognition for brain disease identification have focused on discovering functional characteristics shared across subjects; however, they have paid less attention to individual properties of neural activities that result from different symptoms or degrees of abnormality. In this work, we propose a novel framework that can identify subjects with early-stage mild cognitive impairment (eMCI) and consider individual variability by learning functional relations from automatically selected regions of interest (ROIs) for each subject concurrently. In particular, we devise a deep neural network composed of a temporal embedding module, an ROI selection module, and a disease-identification module. Notably, the ROI selection module is equipped with a reinforcement learning mechanism so it adaptively selects ROIs to facilitate the learning of discriminative feature representations from a temporally embedded blood-oxygen-level-dependent signals. Furthermore, our method allows us to capture the functional relations of a subject-specific ROI subset through the use of a graph-based neural network. Our method considers individual characteristics for diagnosis, as opposed to most conventional methods that identify the same biomarkers across subjects within a group. Based on the ADNI cohort, we validate the effectiveness of our method by presenting the superior performance of our network in eMCI identification. Furthermore, we provide insightful neuroscientific interpretations by analyzing the regions selected for the eMCI classification.

Volumetric Brain Changes in Older Fallers: A Voxel-Based Morphometric Study

Background

Falls are frequent and severe in older adults, especially among those with cognitive impairments due to altered motor control. Which brain areas are affected among fallers remains yet not elucidated. The objective of this cross-sectional analysis was to determine whether the history of falls correlated with focal brain volume reductions in older adults.

Methods

Participants from the MERE study (n = 208; mean, 71.9 ± 5.9 years; 43% female; 38% cognitively healthy, 41% with mild cognitive impairment and 21% with dementia) were asked about their history of falls over the preceding year and received a 1.5-Tesla MRI scan of the brain. Cortical gray and white matter subvolumes were automatically segmented using Statistical Parametric Mapping. Age, gender, use of psychoactive drugs, cognitive status, and total intracranial volume were used as covariates.

Results

Fifty-eight participants (28%) reported history of falls. Fallers were older (P = 0.001), used more often psychoactive drugs (P = 0.008) and had more often dementia (P = 0.004) compared to non-fallers. After adjustment, we found correlations between the history of falls and brain subvolumes; fallers exhibiting larger gray matter subvolumes in striatum, principally in bilateral caudate nucleus, than non-fallers. By stratifying on cognitive status, these neuroanatomical correlates were retrieved only in participants with MCI or dementia. There were no correlations with the subvolumes of white matter.

Conclusion

Older fallers had larger subvolumes in bilateral striatum than non-fallers, principally within the caudate nucleus. This suggests a possible brain adaptative mechanism of falls in people with neurocognitive decline.

Event-Related Potentials, Inhibition, and Risk for Alzheimer's Disease Among Cognitively Intact Elders

BACKGROUND

Despite advances in understanding Alzheimer's disease (AD), prediction of AD prior to symptom onset remains severely limited, even when primary risk factors such as the apolipoprotein E (APOE) ɛ4 allele are known.

OBJECTIVE

Although executive dysfunction is highly prevalent and is a primary contributor to loss of independence in those with AD, few studies have examined neural differences underlying executive functioning as indicators of risk for AD prior to symptom onset, when intervention might be effective.

METHODS

This study examined event-related potential (ERP) differences during inhibitory control in 44 cognitively intact older adults (20 ɛ4+, 24 ɛ4-), relative to 41 young adults. All participants completed go/no-go and stop-signal tasks.

RESULTS

Overall, both older adult groups exhibited slower reaction times and longer ERP latencies compared to young adults. Older adults also had generally smaller N200 and P300 amplitudes, except at frontal electrodes and for N200 stop-signal amplitudes, which were larger in older adults. Considered with intact task accuracy, these findings suggest age-related neural compensation. Although ɛ4 did not distinguish elders during go or no-go tasks, this study uniquely showed that the more demanding stop-signal task was sensitive to ɛ4 differences, despite comparable task and neuropsychological performance with non-carriers. Specifically, ɛ4+ elders had slower frontal N200 latency and larger N200 amplitude, which was most robust at frontal sites, compared with ɛ4-.

CONCLUSION

N200 during a stop-signal task is sensitive to AD risk, prior to any evidence of cognitive dysfunction, suggesting that stop-signal ERPs may be an important protocol addition to neuropsychological testing.

Cognitive aging is associated with redistribution of synaptic weights in the hippocampus

Behaviors that rely on the hippocampus are particularly susceptible to chronological aging, with many aged animals (including humans) maintaining cognition at a young adult-like level, but many others the same age showing marked impairments. It is unclear whether the ability to maintain cognition over time is attributable to brain maintenance, sufficient cognitive reserve, compensatory changes in network function, or some combination thereof. While network dysfunction within the hippocampal circuit of aged, learning-impaired animals is well-documented, its neurobiological substrates remain elusive. Here we show that the synaptic architecture of hippocampal regions CA1 and CA3 is maintained in a young adult-like state in aged rats that performed comparably to their young adult counterparts in both trace eyeblink conditioning and Morris water maze learning. In contrast, among learning-impaired, but equally aged rats, we found that a redistribution of synaptic weights amplifies the influence of autoassociational connections among CA3 pyramidal neurons, yet reduces the synaptic input onto these same neurons from the dentate gyrus. Notably, synapses within hippocampal region CA1 showed no group differences regardless of cognitive ability. Taking the data together, we find the imbalanced synaptic weights within hippocampal CA3 provide a substrate that can explain the abnormal firing characteristics of both CA3 and CA1 pyramidal neurons in aged, learning-impaired rats. Furthermore, our work provides some clarity with regard to how some animals cognitively age successfully, while others' lifespans outlast their "mindspans."

Abnormal cortical regions and subsystems in whole brain functional connectivity of mild cognitive impairment and Alzheimer's disease: a preliminary study

The disease roots of Alzheimer's disease (AD) are unknown. Functional connection (FC) methodology based on functional MRI data is an effective lever to investigate macroscopic neural activity patterns. However, regional properties of brain architecture have been less investigated by special markers of graph indexes in general mental disorders. In terms of the set of the abnormal edges in the FCs matrix, this paper introduces the strength index (S-scores) of region centrality on the principle of holism. Then, the important process is to investigate the S-scores of regions and subsystems in 36 healthy controls, 38 mild cognitive impairment (MCI) patients and 34 AD patients. At the edge level, abnormal FCs is numerically increasing progressively from MCI to AD brains. At the region level, the CUN.L, PAL.R, THA.L, and TPOsup.R regions are highlighted with abnormal S-scores in MCI patients. By comparison, more regions are abnormal in AD patients, which are PreCG.L, INS.R, DCG.L, AMYG.R, IOG.R, FFG.L, PoCG.L, PCUN.R, TPOsup.L, MTG.L, and TPOmid.L. Importantly, the regions in DMN have abnormal S-scores in AD groups. At the module level, the S-scores of frontal, parietal, occipital lobe, and cerebellum are found in MCI and AD patients. Meanwhile, the abnormal lateralization is inferred because of the S-scores of left and top hemisphere in the AD group. Though this is strictly a contrastive study, the S-score may be a meaningful imaging marker for excavating AD psychopathology.

Metacognition of emotion recognition across neurodegenerative diseases

Metacognition (monitoring) of emotion recognition is fundamental for social interactions. Correct recognition of and confidence in the emotional meaning inferred from others' faces are fundamental for guiding and adjusting interpersonal behavior. Yet, although emotion recognition impairments are well documented across neurodegenerative diseases, the role of metacognition in this domain remains poorly understood. Here, we evaluate multimodal neurocognitive markers of metacognition in 83 subjects, encompassing patients with behavioral variant frontotemporal dementia [bvFTD, n = 18], Alzheimer's disease [AD, n = 27], and demographically-matched controls (n = 38). Participants performed a classical facial emotion recognition task and, after each trial, they rated their confidence in their performance. We examined two measures of metacognition: (i) calibration: how well confidence tracks accuracy; and (ii) a metacognitive index (MI) capturing the magnitude of the difference between confidence and accuracy. Then, whole-brain grey matter volume and fMRI-derived resting-state functional connectivity were analyzed to track associations with metacognition. Results showed that metacognition deficits were linked to basic emotion recognition. Metacognition of negative emotions was compromised in patients, especially disgust in bvFTD as well as sadness in AD. Metacognition impairments were associated with reduced volume of fronto-temporo-insular and subcortical areas in bvFTD and fronto-parietal regions in AD. Metacognition deficits were associated with disconnection of large-scale fronto-posterior networks for both groups. This study reveals a link between emotion recognition and metacognition in neurodegenerative diseases. The characterization of metacognitive impairments in bvFTD and AD would be relevant for understanding patients' daily life changes in social behavior.

Preventing dementia? Interventional approaches in mild cognitive impairment

Mild cognitive impairment (MCI) is defined as an intermediate state between normal cognitive aging and dementia. It describes a status of the subjective impression of cognitive decline and objectively detectible memory impairment beyond normal age-related changes. Activities of daily living are not affected. As the population ages, there is a growing need for early, proactive programs that can delay the consequences of dementia and improve the well-being of people with MCI and their caregivers. Various forms and approaches of intervention for older people with MCI have been suggested to delay cognitive decline. Pharmacological as well as non-pharmacological approaches (cognitive, physiological, nutritional supplementation, electric stimulation, psychosocial therapeutic) and multicomponent interventions have been proposed. Interventional approaches in MCI from 2009 to April 2019 concerning the cognitive performance are presented in this review.